System and methods for detecting and mitigating golden saml attacks against federated services

ABSTRACT

A system and methods for detecting and mitigating golden SAML attacks against federated services is provided, comprising an authentication object inspector configured to observe a new authentication object generated by an identity provider, and retrieve the new authentication object; and a hashing engine configured to create a security cookie for each valid authentication session; wherein subsequent access requests accompanied by authentication objects are validated by checking for a valid security cookie.

CROSS-REFERENCE TO RELATED APPLICATIONS

application No. Date Filed Title Current Herewith SYSTEM AND METHODS FORapplication DETECTING AND MITIGATING GOLDEN SAML ATTACKS AGAINSTFEDERATED SERVICES Is a continuation-in-part of: 15/837,845 Dec. 11,2017 DETECTING AND MITIGATING FORGED AUTHENTICATION OBJECT ATTACKS USINGAN ADVANCED CYBER DECISION PLATFORM which claims benefit of, andpriority to: 62/596,105 Dec. 7, 2017 DETECTING AND MITIGATING FORGEDAUTHENTICATION OBJECT ATTACKS USING AN ADVANCED CYBER DECISION PLATFORMand is a continuation-in-part of: 15/825,350 Nov. 29, 2017 USER ANDENTITY BEHAVIORAL Pat. No. Issue Date ANALYSIS USING AN ADVANCED CYBER10,594,714 Mar. 17, 2020 DECISION PLATFORM which is acontinuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCEDPat. No. Issue Date CYBERSECURITY THREAT MITIGATION 10,609,079 Mar. 31,2020 TO ROGUE DEVICES, PRIVILEGE ESCALATION, AND RISK-BASEDVULNERABILITY AND PATCH MANAGEMENT which is a continuation-in-part of:15/655,113 Jul. 20, 2017 ADVANCED CYBERSECURITY THREAT Pat. No. IssueDate MITIGATION USING BEHAVIORAL AND 10,735,456 Aug. 4, 2020 DEEPANALYTICS which is a continuation-in-part of: 15/616,427 Jun. 7, 2017RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR- DRIVENDISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of:14/925,974 Oct. 28, 2015 DETECTION MITIGATION AND REMEDIATION OFCYBERATTACKS EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is acontinuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATIONAND Pat. No. Issue Date REMEDIATION OF CYBERATTACKS 10,248,910 Apr. 2,2019 EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is acontinuation-in-part of: 15/206,195 Jul. 8, 2016 ACCURATE AND DETAILEDMODELING OF SYSTEMS WITH LARGE COMPLEX DATASETS USING A DISTRIBUTEDSIMULATION ENGINE which is a continuation-in-part of: 15/186,453 Jun.18, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESSINFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION which is acontinuation-in-part of: 15/166,158 May 26, 2016 SYSTEM FOR AUTOMATEDCAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR SECURITY ANDCLIENT-FACING INFRASTRUCTURE RELIABILITY which is a continuation-in-partof: 15/141,752 Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED Pat. No. IssueDate CAPTURE, AND ANALYSIS OF BUSINESS 10,860,962 Dec. 8, 2020INFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION whichis a continuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FORCAPTURE, ANALYSIS AND Pat. No. Issue Date STORAGE OF TIME SERIES DATAFROM 10,204,147 Feb. 12, 2019 SENSORS WITH HETEROGENEOUS REPORT INTERVALPROFILES and is also a continuation-in-part of: 14/986,536 Dec. 31, 2015DISTRIBUTED SYSTEM FOR LARGE Pat. No. Issue Date VOLUME DEEP WEB DATA10,210,255 Feb. 19, 2019 EXTRACTION and is also a continuation-in-partof: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGEDATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH the entirespecification of each of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The disclosure relates to the field of network security, particularly tothe detecting and mitigating attacks involving forged authenticationobjects.

Discussion of the State of the Art

As computing moves away from physical and on-premise enterprises towardsmore cloud-based and federated service offerings, a need arises forsingle-sign-on protocols, such as Security Assertion Markup Language(SAML) to provide a user-friendly single-sign-on experience across thefederated service offerings. SAML, for example, uses an identityprovider to generate an authentication object in which a user may use toaccess a plurality of federated service offerings, without the need toauthenticate with each individual service. SAML is a widely usedprotocol in the art, and used applications such as, but is not limitedto, MICROSOFT'S ACTIVE DIRECTORY FEDERATED SERVICES, OKTA, web browsersingle-sign-on, and many cloud service providers (such as AMAZON AWS,AZURE, GOOGLE services, and the like). Although convenient, this createsa security weakness: once an identity provider becomes comprised, anattacker may generate forged authentication objects (called assertionsin SAML terminology) and masquerade as any user, gaining potentiallyfree-reign to do whatever they please of the federated serviceproviders. While traditional cybersecurity approaches may suffice insituations where suspicious activity is noticed, an attacker savvyenough to blend their activity with the usual traffic may go undetectedfor extended periods of time using this forged authentication object.

What is needed is a system that can monitor and analyze event logs toidentify forged SAML assertions indicative of a golden SAML attack, toidentify and mitigate malicious attackers attempting to gain access tofederated services using SAML for authentication.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived, and reduced to practice, asystem and methods for detecting and mitigating golden SAML attacksagainst federated services.

In a typical embodiment, a system for detecting and mitigating forgedauthentication object attacks acts as an external, and non-blockingvalidation service for existing implementations using federated servicesthat use a common identity provider. The system provides services togenerate security cookies (such as QOMPLX® security cookies) forlegitimately-generated authentication objects, and also to checkincoming authentication objects against a database of cryptographichashes of previously-generated assertions within authentication objects(and detecting fraudulent SAML-based authentication attempts bydetecting attempts whose authentication objects' security cookies arenot present in the database of authentication object hashes). The systemmay also allow setting of a plurality of rules to trigger events aftercertain conditions are satisfied.

In one aspect of the invention, a system for detecting and mitigatinggolden SAML attacks against federated services, comprising: anauthentication object inspector comprising at least a processor, amemory, and a plurality of programming instructions stored in the memoryand operating on the processor, wherein the programmable instructions,when operating on the processor, cause the processor to: receive networktraffic comprising a plurality of network packets, the plurality ofnetwork packets comprising at least a first authentication object knownto be generated by an identity provider associated with a federatedservice; store a record of the first authentication object, withattached metadata comprising at least a timestamp of when theauthentication object was received, in a time-series database; generatea security cookie for the first authentication object using a hashingengine; provide the security cookie to the identity provider from whichthe first authentication object was generated; receive a request foraccess to the federated service accompanied by a second authenticationobject; compare a value of an ID string within the second authenticationobject against a value of a corresponding ID string within the storedrecord of the first authentication object; check the secondauthentication object for a valid security cookie; and a hashing enginecomprising a second plurality of programming instructions stored in thememory of, and operating on the processor of, the computing device,wherein the second plurality of programmable instructions, whenoperating on the processor, cause the computing device to: receiveauthentication objects from the authentication object inspector;calculate a security cookie for each authentication object received byperforming at least a plurality of calculations and transformations oneach authentication object received; and return the security cookie foreach authentication object received to the authentication objectinspector, is disclosed.

In another aspect of the invention, a method for detecting golden SAMLattacks against federated services, comprising the steps of: (a)receiving a first authentication object at an authentication objectinspector, the authentication object being generated by an identityprovider; (b) generating a security cookie for the first authenticationobject using a hashing engine; (c) providing the security cookie to theidentity provider from which the first authentication object wasgenerated; (d) receiving a request for access to a federated serviceaccompanied by a second authentication object; and (e) checking thesecond authentication object for a valid security cookie, is disclosed.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1A is a diagram of an exemplary architecture of an advanced cyberdecision platform according to one aspect.

FIG. 1B is a diagram showing a typical operation of accessing a serviceprovider that relies on the SAML protocol for authentication.

FIG. 1C is a diagram showing a method of cyberattack using a forged AO140, which may also be referred to as a “golden SAML” attack.

FIG. 2 is a block diagram illustrating an exemplary system architecturefor a system for detecting and mitigating forged authentication objectattacks according to various embodiments of the invention.

FIG. 3A is a flow diagram of an exemplary function of the businessoperating system in the detection and mitigation of predeterminingfactors leading to and steps to mitigate ongoing cyberattacks.

FIG. 3B is a process diagram showing a general flow of the process usedto detect rogue devices and analyze them for threats.

FIG. 3C is a process diagram showing a general flow of the process usedto detect and prevent privilege escalation attacks on a network.

FIG. 3D is a process diagram showing a general flow of the process usedto manage vulnerabilities associated with patches to network software.

FIGS. 4A and 4B are process diagrams showing business operating systemfunctions in use to mitigate cyberattacks.

FIG. 5 is a process flow diagram of a method for segmenting cyberattackinformation to appropriate corporation parties.

FIG. 6 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph, according to one aspect.

FIG. 7 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph, according to one aspect.

FIG. 8 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph, according to one aspect.

FIG. 9 is a diagram of an exemplary architecture for a user and entitybehavioral analysis system, according to one aspect.

FIG. 10 is a flow diagram of an exemplary method for cybersecuritybehavioral analytics, according to one aspect.

FIG. 11 is a flow diagram of an exemplary method for measuring theeffects of cybersecurity attacks, according to one aspect.

FIG. 12 is a flow diagram of an exemplary method for continuouscybersecurity monitoring and exploration, according to one aspect.

FIG. 13 is a flow diagram of an exemplary method for mapping acyber-physical system graph (CPG), according to one aspect.

FIG. 14 is a flow diagram of an exemplary method for continuous networkresilience scoring, according to one aspect.

FIG. 15 is a flow diagram of an exemplary method for cybersecurityprivilege oversight, according to one aspect.

FIG. 16 is a flow diagram of an exemplary method for cybersecurity riskmanagement, according to one aspect.

FIG. 17 is a flow diagram of an exemplary method for mitigatingcompromised credential threats, according to one aspect.

FIG. 18 is a flow diagram of an exemplary method for dynamic network androgue device discovery, according to one aspect.

FIG. 19 is a flow diagram of an exemplary method for attack detection,according to one aspect.

FIG. 20 is a flow diagram of an exemplary method for risk-basedvulnerability and patch management, according to one aspect.

FIG. 21 is a flow diagram of an exemplary method for establishing groupsof users according to one aspect.

FIG. 22 is a flow diagram of an exemplary method for monitoring groupsfor anomalous behavior, according to one aspect.

FIG. 23 is a flow diagram for an exemplary method for handing adetection of anomalous behavior, according to one aspect.

FIG. 24 is a flow diagram illustrating an exemplary method forprocessing a new user connection, according to one aspect.

FIG. 25 is a flow diagram illustrating an exemplary method for verifyingthe authenticity of an authentication object, according to one aspect.

FIG. 26 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device used in various embodiments of theinvention.

FIG. 27 is a block diagram illustrating an exemplary logicalarchitecture for a client device, according to various embodiments ofthe invention.

FIG. 28 is a block diagram illustrating an exemplary architecturalarrangement of clients, servers, and external services, according tovarious embodiments of the invention.

FIG. 29 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device used in various embodiments of theinvention.

FIG. 30 is a flow diagram illustrating a method for detecting a goldenSAML attack by comparing IDs in event logs, according to an aspect ofthe invention.

FIG. 31 is a flow diagram illustrating a method for detecting a goldenSAML attack using session tagging, according to an aspect of theinvention.

FIG. 32 is a message flow diagram illustrating a valid SAMLauthentication session for a federated service.

FIG. 33 is a message flow diagram illustrating a golden SAML attackusing a forged assertion within an authentication object to gain accessto a federated service.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system andmethods for detecting and mitigating golden SAML attacks againstfederated services.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

As used herein, “graph” is a representation of information andrelationships, where each primary unit of information makes up a “node”or “vertex” of the graph and the relationship between two nodes makes upan edge of the graph. Nodes can be further qualified by the connectionof one or more descriptors or “properties” to that node. For example,given the node “James R,” name information for a person, qualifyingproperties might be “183 cm tall”, “DOB 08/13/1965” and “speaksEnglish”. Similar to the use of properties to further describe theinformation in a node, a relationship between two nodes that forms anedge can be qualified using a “label”. Thus, given a second node “ThomasG,” an edge between lames R″ and “Thomas G” that indicates that the twopeople know each other might be labeled “knows.” When graph theorynotation (Graph=(Vertices, Edges)) is applied this situation, the set ofnodes are used as one parameter of the ordered pair,V and the set of 2element edge endpoints are used as the second parameter of the orderedpair, E. When the order of the edge endpoints within the pairs of E isnot significant, for example, the edge James R, Thomas G is equivalentto Thomas G, James R, the graph is designated as “undirected.” Undercircumstances when a relationship flows from one node to another in onedirection, for example James R is “taller” than Thomas G, the order ofthe endpoints is significant. Graphs with such edges are designated as“directed.” In the distributed computational graph system,transformations within transformation pipeline are represented asdirected graph with each transformation comprising a node and the outputmessages between transformations comprising edges. Distributedcomputational graph stipulates the potential use of non-lineartransformation pipelines which are programmatically linearized. Suchlinearization can result in exponential growth of resource consumption.The most sensible approach to overcome possibility is to introduce newtransformation pipelines just as they are needed, creating only thosethat are ready to compute. Such method results in transformation graphswhich are highly variable in size and node, edge composition as thesystem processes data streams. Those familiar with the art will realizethat transformation graph may assume many shapes and sizes with a vasttopography of edge relationships. The examples given were chosen forillustrative purposes only and represent a small number of the simplestof possibilities. These examples should not be taken to define thepossible graphs expected as part of operation of the invention

As used herein, “transformation” is a function performed on zero or morestreams of input data which results in a single stream of output whichmay or may not then be used as input for another transformation.Transformations may comprise any combination of machine, human ormachine-human interactions Transformations need not change data thatenters them, one example of this type of transformation would be astorage transformation which would receive input and then act as a queuefor that data for subsequent transformations. As implied above, aspecific transformation may generate output data in the absence of inputdata. A time stamp serves as a example. In the invention,transformations are placed into pipelines such that the output of onetransformation may serve as an input for another. These pipelines canconsist of two or more transformations with the number oftransformations limited only by the resources of the system.Historically, transformation pipelines have been linear with eachtransformation in the pipeline receiving input from one antecedent andproviding output to one subsequent with no branching or iteration. Otherpipeline configurations are possible. The invention is designed topermit several of these configurations including, but not limited to:linear, afferent branch, efferent branch and cyclical.

A “database” or “data storage subsystem” (these terms may be consideredsubstantially synonymous), as used herein, is a system adapted for thelong-term storage, indexing, and retrieval of data, the retrievaltypically being via some sort of querying interface or language.“Database” may be used to refer to relational database managementsystems known in the art, but should not be considered to be limited tosuch systems. Many alternative database or data storage systemtechnologies have been, and indeed are being, introduced in the art,including but not limited to distributed non-relational data storagesystems such as Hadoop, column-oriented databases, in-memory databases,and the like. While various aspects may preferentially employ one oranother of the various data storage subsystems available in the art (oravailable in the future), the invention should not be construed to be solimited, as any data storage architecture may be used according to theaspects. Similarly, while in some cases one or more particular datastorage needs are described as being satisfied by separate components(for example, an expanded private capital markets database and aconfiguration database), these descriptions refer to functional uses ofdata storage systems and do not refer to their physical architecture.For instance, any group of data storage systems of databases referred toherein may be included together in a single database management systemoperating on a single machine, or they may be included in a singledatabase management system operating on a cluster of machines as isknown in the art. Similarly, any single database (such as an expandedprivate capital markets database) may be implemented on a singlemachine, on a set of machines using clustering technology, on severalmachines connected by one or more messaging systems known in the art, orin a master/slave arrangement common in the art. These examples shouldmake clear that no particular architectural approaches to databasemanagement is preferred according to the invention, and choice of datastorage technology is at the discretion of each implementer, withoutdeparting from the scope of the invention as claimed.

A “data context”, as used herein, refers to a set of argumentsidentifying the location of data. This could be a Rabbit queue, a .csvfile in cloud-based storage, or any other such location reference excepta single event or record. Activities may pass either events or datacontexts to each other for processing. The nature of a pipeline allowsfor direct information passing between activities, and data locations orfiles do not need to be predetermined at pipeline start.

A “pipeline”, as used herein and interchangeably referred to as a “datapipeline” or a “processing pipeline”, refers to a set of data streamingactivities and batch activities. Streaming and batch activities can beconnected indiscriminately within a pipeline. Events will flow throughthe streaming activity actors in a reactive way. At the junction of astreaming activity to batch activity, there will exist aStreamBatchProtocol data object. This object is responsible fordetermining when and if the batch process is run. One or more of threepossibilities can be used for processing triggers: regular timinginterval, every N events, or optionally an external trigger. The eventsare held in a queue or similar until processing. Each batch activity maycontain a “source” data context (this may be a streaming context if theupstream activities are streaming), and a “destination” data context(which is passed to the next activity). Streaming activities may have anoptional “destination” streaming data context (optional meaning:caching/persistence of events vs. ephemeral), though this should not bepart of the initial implementation.

Conceptual Architecture

FIG. 1A is a diagram of an exemplary architecture of an advanced cyberdecision platform (ACDP) 100 according to one aspect. Client access tothe system 105 for specific data entry, system control and forinteraction with system output such as automated predictive decisionmaking and planning and alternate pathway simulations, occurs throughthe system's distributed, extensible high bandwidth cloud interface 110which uses a versatile, robust web application driven interface for bothinput and display of client-facing information via network 107 andoperates a data store 112 such as, but not limited to MONGODB™,COUCHDB™, CASSANDRA™ or REDIS™ according to various arrangements. Muchof the business data analyzed by the system both from sources within theconfines of the client business, and from cloud based sources, alsoenter the system through the cloud interface 110, data being passed tothe connector module 135 which may possess the API routines 135 a neededto accept and convert the external data and then pass the normalizedinformation to other analysis and transformation components of thesystem, the directed computational graph module 155, high volume webcrawler module 115, multidimensional time series database 120 and thegraph stack service 145. The directed computational graph module 155retrieves one or more streams of data from a plurality of sources, whichincludes, but is in no way not limited to, a plurality of physicalsensors, network service providers, web based questionnaires andsurveys, monitoring of electronic infrastructure, crowd sourcingcampaigns, and human input device information. Within the directedcomputational graph module 155, data may be split into two identicalstreams in a specialized pre-programmed data pipeline 155 a, wherein onesub-stream may be sent for batch processing and storage while the othersub-stream may be reformatted for transformation pipeline analysis. Thedata is then transferred to the general transformer service module 160for linear data transformation as part of analysis or the decomposabletransformer service module 150 for branching or iterativetransformations that are part of analysis. The directed computationalgraph module 155 represents all data as directed graphs where thetransformations are nodes and the result messages betweentransformations edges of the graph. The high volume web crawling module115 uses multiple server hosted preprogrammed web spiders, which whileautonomously configured are deployed within a web scraping framework 115a of which SCRAPY™ is an example, to identify and retrieve data ofinterest from web based sources that are not well tagged by conventionalweb crawling technology. The multiple dimension time series data storemodule 120 may receive streaming data from a large plurality of sensorsthat may be of several different types. The multiple dimension timeseries data store module may also store any time series data encounteredby the system such as but not limited to enterprise network usage data,component and system logs, performance data, network service informationcaptures such as, but not limited to news and financial feeds, and salesand service related customer data. The module is designed to accommodateirregular and high volume surges by dynamically allotting networkbandwidth and server processing channels to process the incoming data.Inclusion of programming wrappers for languages examples of which are,but not limited to C++, PERL, PYTHON, and ERLANG™ allows sophisticatedprogramming logic to be added to the default function of themultidimensional time series database 120 without intimate knowledge ofthe core programming, greatly extending breadth of function. Dataretrieved by the multidimensional time series database 120 and the highvolume web crawling module 115 may be further analyzed and transformedinto task optimized results by the directed computational graph 155 andassociated general transformer service 150 and decomposable transformerservice 160 modules. Alternately, data from the multidimensional timeseries database and high volume web crawling modules may be sent, oftenwith scripted cuing information determining important vertexes 145 a, tothe graph stack service module 145 which, employing standardizedprotocols for converting streams of information into graphrepresentations of that data, for example, open graph internettechnology although the invention is not reliant on any one standard.Through the steps, the graph stack service module 145 represents data ingraphical form influenced by any pre-determined scripted modifications145 a and stores it in a graph-based data store 145 b such as GIRAPH™ ora key value pair type data store REDIS™, or RIAK™, among others, all ofwhich are suitable for storing graph-based information.

Results of the transformative analysis process may then be combined withfurther client directives, additional business rules and practicesrelevant to the analysis and situational information external to thealready available data in the automated planning service module 130which also runs powerful information theory 130 a based predictivestatistics functions and machine learning algorithms to allow futuretrends and outcomes to be rapidly forecast based upon the current systemderived results and choosing each a plurality of possible businessdecisions. The using all available data, the automated planning servicemodule 130 may propose business decisions most likely to result is themost favorable business outcome with a usably high level of certainty.Closely related to the automated planning service module in the use ofsystem derived results in conjunction with possible externally suppliedadditional information in the assistance of end user business decisionmaking, the action outcome simulation module 125 with its discrete eventsimulator programming module 125 a coupled with the end user facingobservation and state estimation service 140 which is highly scriptable140 b as circumstances require and has a game engine 140 a to morerealistically stage possible outcomes of business decisions underconsideration, allows business decision makers to investigate theprobable outcomes of choosing one pending course of action over anotherbased upon analysis of the current available data.

For example, the Information Assurance department is notified by thesystem 100 that principal X is using credentials K (Kerberos PrincipalKey) never used by it before to access service Y. Service Y utilizesthese same credentials to access secure data on data store Z. Thiscorrectly generates an alert as suspicious lateral movement through thenetwork and will recommend isolation of X and Y and suspension of Kbased on continuous baseline network traffic monitoring by themultidimensional time series data store 120 programmed to process suchdata 120 a, rigorous analysis of the network baseline by the directedcomputational graph 155 with its underlying general transformer servicemodule 160 and decomposable transformer service module 150 inconjunction with the AI and primed machine learning capabilities 130 aof the automated planning service module 130 which had also received andassimilated publicly available from a plurality of sources through themulti-source connection APIs of the connector module 135. Ad hocsimulations of these traffic patterns are run against the baseline bythe action outcome simulation module 125 and its discrete eventsimulator 125 a which is used here to determine probability space forlikelihood of legitimacy. The system 100, based on this data andanalysis, was able to detect and recommend mitigation of a cyberattackthat represented an existential threat to all business operations,presenting, at the time of the attack, information most needed for anactionable plan to human analysts at multiple levels in the mitigationand remediation effort through use of the observation and stateestimation service 140 which had also been specifically preprogrammed tohandle cybersecurity events 140 b.

A forged authentication object detection and mitigation service 910 maybe used to detect and mitigate cyberattacks stemming from the use ofauthentication objects generated by an attacker. Service 910 isdiscussed in further detail below in FIG. 2.

According to one aspect, the advanced cyber decision platform, aspecifically programmed usage of the business operating system,continuously monitors a client enterprise's normal network activity forbehaviors such as but not limited to normal users on the network,resources accessed by each user, access permissions of each user,machine to machine traffic on the network, sanctioned external access tothe core network and administrative access to the network's identity andaccess management servers in conjunction with real-time analyticsinforming knowledge of cyberattack methodology. The system then usesthis information for two purposes: First, the advanced computationalanalytics and simulation capabilities of the system are used to provideimmediate disclosure of probable digital access points both at thenetwork periphery and within the enterprise's information transfer andtrust structure and recommendations are given on network changes thatshould be made to harden it prior to or during an attack. Second, theadvanced cyber decision platform continuously monitors the network inreal-time both for types of traffic and through techniques such as deeppacket inspection for pre-decided analytically significant deviation inuser traffic for indications of known cyberattack vectors such as, butnot limited to, ACTIVE DIRECTORY™/Kerberos pass-the-ticket attack,ACTIVE DIRECTORY™/Kerberos pass-the-hash attack and the related ACTIVEDIRECTORY™/Kerberos overpass-the-hash attack, ACTIVE DIRECTORY™/KerberosSkeleton Key, ACTIVE DIRECTORY™/Kerberos golden and silver ticketattack, privilege escalation attack, compromised user credentials,ransomware disk attacks, and SAML forged authentication object attack(also may be referred to as golden SAML). When suspicious activity at alevel signifying an attack (for example, including but not limited toskeleton key attacks, pass-the-hash attacks, or attacks via compromiseduser credentials) is determined, the system issues action-focused alertinformation to all predesignated parties specifically tailored to theirroles in attack mitigation or remediation and formatted to providepredictive attack modeling based upon historic, current, and contextualattack progression analysis such that human decision makers can rapidlyformulate the most effective courses of action at their levels ofresponsibility in command of the most actionable information with aslittle distractive data as possible. The system then issues defensivemeasures in the most actionable form to end the attack with the leastpossible damage and exposure. All attack data are persistently storedfor later forensic analysis.

FIG. 1B is a diagram showing a typical operation of accessing a serviceprovider that relies on the SAML protocol for authentication 120, asused in the art. A user, using a computing device, may request access toa one of a plurality of federated servers, and through the steps listed121, an AO is generated for the user from an identity provider (IdP).The user may then be granted access to, not only the service that wasoriginally requested, but any trusted partners as well.

FIG. 1C is a diagram showing a method of cyberattack using a forged AO140, which may also be referred to as a “golden SAML” attack, as knownin the art. Through steps 141, an attacker, using information acquiredfrom a compromised IdP, may generate his own AO, bypassing the need toauthenticate with an IdP. Once the AO has been generated, the attackermay assume the role of any user registered with the IdP, and freelyaccess the service providers. While using various systems and methodsdisclosed herein may be sufficient, additional measures for detectingand mitigating forged authentication object attacks may be required.

FIG. 2 is a block diagram illustrating an exemplary system architecture900 for a system 910 for detecting and mitigating forged authenticationobject attacks according to various embodiments of the invention.Architecture 900 may comprise system 910 acting as a non-blockingintermediary between a connecting user 920, a plurality of federatedservice providers (SP) 921 a-n, an identity provider (IdP) 922, and anadministrative user 923.

System 910 may be configured to verifying incoming connections when theuser has an AO, and also keeps track of legitimately generated AO's.System 910 may comprise an AO inspector 911, a hashing engine 912, anevent-condition-action (ECA) rules engine 913, and a data store 914.

AO inspector 911 may be configured to use faculties of ACDP 100, forexample DCG module 155 and associated transformer modules to analyze andprocess AO's associated with incoming connections, and observation andstate estimation services 140 to monitor connections for incoming AO's.Incoming AO's may be retrieved for further analysis by system 910.

Hashing engine 912 may be configured to calculate a cryptographic hashfor AOs generated by identity provider 922 using functions of ACDP 100,such as DCG module 155, generate a cryptographic hash for both incomingAO's (for analysis purposes), and new AO's created by IdP 922. A one-wayhash may be used to allow protecting of sensitive information containedin the AO, but preserving uniqueness of each AO. Generated hashes may bestored in data store 914. Hashing engine may also run a hash checkfunction, used for validating incoming AO's.

ECA rules engine 913 may be used by a network administrator to createand manage ECA rules that may trigger actions and queries in the eventof detection of a forged AO. Rules may be for example, tracking andlogging the actions of the suspicious user, deferring the suspiciousconnection, and the like. Rules may be nested to create a complex flowof various conditional checks and actions to create a set of “circuitbreaker” checks to further ascertain the connection, or try and resolvethe matter automatically before notifying a human network administrator.

Data store 914 may be a graph and time-series hybrid database, such asmultidimensional time-series data store 120 or data store 112, thatstores hashes, ECA rules, log data, and the like, and may be quickly andefficiently queried and processed using ACDP 100.

Federated service providers 921 a-n may comprise a group of trustedservice partners that may share a common IdP 922 in which user 920 maywish to access. Federated service providers 921 a-n may be, forinstance, services employing MICROSOFT'S ACTIVE DIRECTORY FEDERATEDSERVICES (AS DS), AZURE AD, OKTA, many web browser single-sign-on (SSO)implementations, cloud service provides (such as, AMAZON AWS, AZURE, andGOOGLE), and the like.

FIG. 3A is a flow diagram of an exemplary function of the businessoperating system in the detection and mitigation of predeterminingfactors leading to and steps to mitigate ongoing cyberattacks 200. Thesystem continuously retrieves network traffic data 201 which may bestored and preprocessed by the multidimensional time series data store120 and its programming wrappers 120 a. All captured data are thenanalyzed to predict the normal usage patterns of network nodes such asinternal users, network connected systems and equipment and sanctionedusers external to the enterprise boundaries for example off-siteemployees, contractors and vendors, just to name a few likelyparticipants. Of course, normal other network traffic may also be knownto those skilled in the field, the list given is not meant to beexclusive and other possibilities would not fall outside the design ofthe invention. Analysis of network traffic may include graphicalanalysis of parameters such as network item to network usage usingspecifically developed programming in the graphstack service 145, 145 a,analysis of usage by each network item may be accomplished byspecifically pre-developed algorithms associated with the directedcomputational graph module 155, general transformer service module 160and decomposable service module 150, depending on the complexity of theindividual usage profile 201. These usage pattern analyses, inconjunction with additional data concerning an enterprise's networktopology; gateway firewall programming; internal firewall configuration;directory services protocols and configuration; and permissions profilesfor both users and for access to sensitive information, just to list afew non-exclusive examples may then be analyzed further within theautomated planning service module 130, where machine learning techniqueswhich include but are not limited to information theory statistics 130 amay be employed and the action outcome simulation module 125,specialized for predictive simulation of outcome based on current data125 a may be applied to formulate a current, up-to-date and continuouslyevolving baseline network usage profile 202. This same data would becombined with up-to-date known cyberattack methodology reports, possiblyretrieved from several divergent and exogenous sources through the useof the multi-application programming interface aware connector module135 to present preventative recommendations to the enterprise decisionmakers for network infrastructure changes, physical andconfiguration-based to cost effectively reduce the probability of acyberattack and to significantly and most cost effectively mitigate dataexposure and loss in the event of attack 203, 204.

While some of these options may have been partially available aspiecemeal solutions in the past, we believe the ability to intelligentlyintegrate the large volume of data from a plurality of sources on anongoing basis followed by predictive simulation and analysis of outcomebased upon that current data such that actionable, business practiceefficient recommendations can be presented is both novel and necessaryin this field.

Once a comprehensive baseline profile of network usage using allavailable network traffic data has been formulated, the specificallytasked business operating system continuously polls the incoming trafficdata for activities anomalous to that baseline as determined bypre-designated boundaries 205. Examples of anomalous activities mayinclude a user attempting to gain access several workstations or serversin rapid succession, or a user attempting to gain access to a domainserver of server with sensitive information using random userIDs oranother user's userID and password, or attempts by any user to bruteforce crack a privileged user's password, or replay of recently issuedACTIVE DIRECTORY™/Kerberos ticket granting tickets, or using a forgedSAML AO, or the presence on any known, ongoing exploit on the network orthe introduction of known malware to the network, just to name a verysmall sample of the cyberattack profiles known to those skilled in thefield. The invention, being predictive as well as aware of knownexploits is designed to analyze any anomalous network behavior,formulate probable outcomes of the behavior, and to then issue anyneeded alerts regardless of whether the attack follows a publishedexploit specification or exhibits novel characteristics deviant tonormal network practice. Once a probable cyberattack is detected, thesystem then is designed to get needed information to responding parties206 tailored, where possible, to each role in mitigating the attack anddamage arising from it 207. This may include the exact subset ofinformation included in alerts and updates and the format in which theinformation is presented which may be through the enterprise's existingsecurity information and event management system. Networkadministrators, then, might receive information such as but not limitedto where on the network the attack is believed to have originated, whatsystems are believed currently affected, predictive information on wherethe attack may progress, what enterprise information is at risk andactionable recommendations on repelling the intrusion and mitigating thedamage, whereas a chief information security officer may receive alertincluding but not limited to a timeline of the cyberattack, the servicesand information believed compromised, what action, if any has been takento mitigate the attack, a prediction of how the attack may unfold andthe recommendations given to control and repel the attack 207, althoughall parties may access any network and cyberattack information for whichthey have granted access at any time, unless compromise is suspected.Other specifically tailored updates may be issued by the system 206,207.

FIG. 3B is a process diagram showing a general flow of the process usedto detect rogue devices and analyze them for threats 220. Whenever adevice is connected to the network 221, the connection is immediatelysent to the rogue device detector 222 for analysis. As disclosed belowat 300, the advanced cyber decision platform uses machine learningalgorithms to analyze system-wide data to detect threats. The connecteddevice is analyzed 223 to assess its device type, settings, andcapabilities, the sensitivity of the data stored on the server to whichthe device wishes to connect, network activity, server logs, remotequeries, and a multitude of other data to determine the level of threatassociated with the device. If the threat reaches a certain level 224,the device is automatically prevented from accessing the network 225,and the system administrator is notified of the potential threat, alongwith contextually-based, tactical recommendations for optimal responsebased on potential impact 226. Otherwise, the device is allowed toconnect to the network 227.

FIG. 3C is a process diagram showing a general flow of the process usedto detect and prevent privilege escalation attacks on a network 240.When access to a server within the network is requested using a digitalsignature or AO 241, the connection is immediately sent to the privilegeescalation attack detector 242 for analysis. As disclosed below at 300,the advanced cyber decision platform uses machine learning algorithms toanalyze system-wide data to detect threats. The access request isanalyzed 243 to assess the validity of the access request using thedigital signature validation, plus other system-wide information such asthe sensitivity of the server being accessed, the newness of the digitalsignature or AO, the digital signature's or AO's prior usage, and othermeasures of the digital signature's or AO's validity. If the assessmentdetermines that the access request represents a significant threat 244,even despite the Kerberos validation of the digital signature orvalidation of a SAML AO, the access request is automatically denied 245,and the system administrator is notified of the potential threat, alongwith contextually-based, tactical recommendations for optimal responsebased on potential impact 246. Otherwise, the access request is granted247.

FIG. 3D is a process diagram showing a general flow of the process usedto manage vulnerabilities associated with patches to network software260. As part of a continuously-operating risk-based vulnerability andpatch management monitor 261, data is gathered from both sourcesexternal to the network 262 and internal to the network 263. Asdisclosed below at 300, the advanced cyber decision platform usesmachine learning algorithms to analyze system-wide data to detectthreats. The data is analyzed 264 to determine whether networkvulnerabilities exist for which a patch has not yet been created and/orapplied. If the assessment determines that such a vulnerability exists265, whether or not all software has been patched according tomanufacturer recommendations, the system administrator is notified ofthe potential vulnerability, along with contextually-based, tacticalrecommendations for optimal response based on potential impact 266.Otherwise, network activity is allowed to continue normally 267.

FIGS. 4A and 4B are process diagrams showing a general flow 300 ofbusiness operating system functions in use to mitigate cyberattacks.Input network data which may include network flow patterns 321, theorigin and destination of each piece of measurable network traffic 322,system logs from servers and workstations on the network 323, endpointdata 323 a, any security event log data from servers or availablesecurity information and event (SIEM) systems 324, external threatintelligence feeds 324 a, identity or assessment context 325, externalnetwork health or cybersecurity feeds 326, Kerberos domain controller orACTIVE DIRECTORY™ server logs or instrumentation 327 and business unitperformance related data 328, among many other possible data types forwhich the invention was designed to analyze and integrate, may pass into315 the business operating system 310 for analysis as part of its cybersecurity function. These multiple types of data from a plurality ofsources may be transformed for analysis 311, 312 using at least one ofthe specialized cybersecurity, risk assessment or common functions ofthe business operating system in the role of cybersecurity system, suchas, but not limited to network and system user privilege oversight 331,network and system user behavior analytics 332, attacker and defenderaction timeline 333, SIEM integration and analysis 334, dynamicbenchmarking 335, and incident identification and resolution performanceanalytics 336 among other possible cybersecurity functions; value atrisk (VAR) modeling and simulation 341, anticipatory vs. reactive costestimations of different types of data breaches to establish priorities342, work factor analysis 343 and cyber event discovery rate 344 as partof the system's risk analytics capabilities; and the ability to formatand deliver customized reports and dashboards 351, perform generalized,ad hoc data analytics on demand 352, continuously monitor, process andexplore incoming data for subtle changes or diffuse informationalthreads 353 and generate cyber-physical systems graphing 354 as part ofthe business operating system's common capabilities. Output 317 can beused to configure network gateway security appliances 361, to assist inpreventing network intrusion through predictive change to infrastructurerecommendations 362, to alert an enterprise of ongoing cyberattack earlyin the attack cycle, possibly thwarting it but at least mitigating thedamage 368, to record compliance to standardized guidelines or SLArequirements 363, to continuously probe existing network infrastructureand issue alerts to any changes which may make a breach more likely 364,suggest solutions to any domain controller weaknesses detected 365,detect presence of malware 366, and perform one time or continuousvulnerability scanning depending on client directives 367. Theseexamples are, of course, only a subset of the possible uses of thesystem, they are exemplary in nature and do not reflect any boundariesin the capabilities of the invention.

FIG. 5 is a process flow diagram of a method for segmenting cyberattackinformation to appropriate corporation parties 400. As previouslydisclosed 200, 351, one of the strengths of the advanced cyber-decisionplatform is the ability to finely customize reports and dashboards tospecific audiences, concurrently is appropriate. This customization ispossible due to the devotion of a portion of the business operatingsystem's programming specifically to outcome presentation by moduleswhich include the observation and state estimation service 140 with itsgame engine 140 a and script interpreter 140 b. In the setting ofcybersecurity, issuance of specialized alerts, updates and reports maysignificantly assist in getting the correct mitigating actions done inthe most timely fashion while keeping all participants informed atpredesignated, appropriate granularity. Upon the detection of acyberattack by the system 401 all available information about theongoing attack and existing cybersecurity knowledge are analyzed,including through predictive simulation in near real time 402 to developboth the most accurate appraisal of current events and actionablerecommendations concerning where the attack may progress and how it maybe mitigated. The information generated in totality is often more thanany one group needs to perform their mitigation tasks. At this point,during a cyberattack, providing a single expansive and all inclusivealert, dashboard image, or report may make identification and actionupon the crucial information by each participant more difficult,therefore the cybersecurity focused arrangement may create multipletargeted information streams each concurrently designed to produce mostrapid and efficacious action throughout the enterprise during the attackand issue follow-up reports with and recommendations or information thatmay lead to long term changes afterward 403. Examples of groups that mayreceive specialized information streams include but may not be limitedto front line responders during the attack 404, incident forensicssupport both during and after the attack 405, chief information securityofficer 406 and chief risk officer 407 the information sent to thelatter two focused to appraise overall damage and to implement bothmitigating strategy and preventive changes after the attack. Front lineresponders may use the cyber-decision platform's analyzed, transformedand correlated information specifically sent to them 404a to probe theextent of the attack, isolate such things as: the predictive attacker'sentry point onto the enterprise's network, the systems involved or thepredictive ultimate targets of the attack and may use the simulationcapabilities of the system to investigate alternate methods ofsuccessfully ending the attack and repelling the attackers in the mostefficient manner, although many other queries known to those skilled inthe art are also answerable by the invention. Simulations run may alsoinclude the predictive effects of any attack mitigating actions onnormal and critical operation of the enterprise's IT systems andcorporate users. Similarly, a chief information security officer may usethe cyber-decision platform to predictively analyze 406 a what corporateinformation has already been compromised, predictively simulate theultimate information targets of the attack that may or may not have beencompromised and the total impact of the attack what can be done now andin the near future to safeguard that information. Further, duringretrospective forensic inspection of the attack, the forensic respondermay use the cyber-decision platform 405 a to clearly and completely mapthe extent of network infrastructure through predictive simulation andlarge volume data analysis. The forensic analyst may also use theplatform's capabilities to perform a time series and infrastructuralspatial analysis of the attack's progression with methods used toinfiltrate the enterprise's subnets and servers. Again, the chief riskofficer would perform analyses of what information 407 a was stolen andpredictive simulations on what the theft means to the enterprise as timeprogresses. Additionally, the system's predictive capabilities may beemployed to assist in creation of a plan for changes of the ITinfrastructural that should be made that are optimal for remediation ofcybersecurity risk under possibly limited enterprise budgetaryconstraints in place at the company so as to maximize financial outcome.

FIG. 6 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph 500, according to one aspect. Accordingto the aspect, a DCG 500 may comprise a pipeline orchestrator 501 thatmay be used to perform a variety of data transformation functions ondata within a processing pipeline, and may be used with a messagingsystem 510 that enables communication with any number of variousservices and protocols, relaying messages and translating them as neededinto protocol-specific API system calls for interoperability withexternal systems (rather than requiring a particular protocol or serviceto be integrated into a DCG 500).

Pipeline orchestrator 501 may spawn a plurality of child pipelineclusters 502 a-b, which may be used as dedicated workers forstreamlining parallel processing. In some arrangements, an entire dataprocessing pipeline may be passed to a child cluster 502 a for handling,rather than individual processing tasks, enabling each child cluster 502a-b to handle an entire data pipeline in a dedicated fashion to maintainisolated processing of different pipelines using different cluster nodes502 a-b. Pipeline orchestrator 501 may provide a software API forstarting, stopping, submitting, or saving pipelines. When a pipeline isstarted, pipeline orchestrator 501 may send the pipeline information toan available worker node 502 a-b, for example using AKKA™ clustering.For each pipeline initialized by pipeline orchestrator 501, a reportingobject with status information may be maintained. Streaming activitiesmay report the last time an event was processed, and the number ofevents processed. Batch activities may report status messages as theyoccur. Pipeline orchestrator 501 may perform batch caching using, forexample, an IGFS™ caching filesystem. This allows activities 512 a-dwithin a pipeline 502 a-b to pass data contexts to one another, with anynecessary parameter configurations.

A pipeline manager 511 a-b may be spawned for every new runningpipeline, and may be used to send activity, status, lifecycle, and eventcount information to the pipeline orchestrator 501. Within a particularpipeline, a plurality of activity actors 512 a-d may be created by apipeline manager 511 a-b to handle individual tasks, and provide outputto data services 522 a-d. Data models used in a given pipeline may bedetermined by the specific pipeline and activities, as directed by apipeline manager 511 a-b. Each pipeline manager 511 a-b controls anddirects the operation of any activity actors 512 a-d spawned by it. Apipeline process may need to coordinate streaming data between tasks.For this, a pipeline manager 511 a-b may spawn service connectors todynamically create TCP connections between activity instances 512 a-d.Data contexts may be maintained for each individual activity 512 a-d,and may be cached for provision to other activities 512 a-d as needed. Adata context defines how an activity accesses information, and anactivity 512 a-d may process data or simply forward it to a next step.Forwarding data between pipeline steps may route data through astreaming context or batch context.

A client service cluster 530 may operate a plurality of service actors521 a-d to serve the requests of activity actors 512 a-d, ideallymaintaining enough service actors 521 a-d to support each activity perthe service type. These may also be arranged within service clusters 520a-d, in a manner similar to the logical organization of activity actors512 a-d within clusters 502 a-b in a data pipeline. A logging service530 may be used to log and sample DCG requests and messages duringoperation while notification service 540 may be used to receive alertsand other notifications during operation (for example to alert onerrors, which may then be diagnosed by reviewing records from loggingservice 530), and by being connected externally to messaging system 510,logging and notification services can be added, removed, or modifiedduring operation without impacting DCG 500. A plurality of DCG protocols550 a-b may be used to provide structured messaging between a DCG 500and messaging system 510, or to enable messaging system 510 todistribute DCG messages across service clusters 520 a-d as shown. Aservice protocol 560 may be used to define service interactions so thata DCG 500 may be modified without impacting service implementations. Inthis manner it can be appreciated that the overall structure of a systemusing an actor-driven DCG 500 operates in a modular fashion, enablingmodification and substitution of various components without impactingother operations or requiring additional reconfiguration.

FIG. 7 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph 500, according to one aspect. Accordingto the aspect, a variant messaging arrangement may utilize messagingsystem 510 as a messaging broker using a streaming protocol 610,transmitting and receiving messages immediately using messaging system510 as a message broker to bridge communication between service actors521 a-b as needed. Alternately, individual services 522 a-b maycommunicate directly in a batch context 620, using a data contextservice 630 as a broker to batch-process and relay messages betweenservices 522 a-b.

FIG. 8 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph 500, according to one aspect. Accordingto the aspect, a variant messaging arrangement may utilize a serviceconnector 710 as a central message broker between a plurality of serviceactors 521 a-b, bridging messages in a streaming context 610 while adata context service 630 continues to provide direct peer-to-peermessaging between individual services 522 a-b in a batch context 620.

It should be appreciated that various combinations and arrangements ofthe system variants described above (referring to FIGS. 1A-8) may bepossible, for example using one particular messaging arrangement for onedata pipeline directed by a pipeline manager 511 a-b, while anotherpipeline may utilize a different messaging arrangement (or may notutilize messaging at all). In this manner, a single DCG 500 and pipelineorchestrator 501 may operate individual pipelines in the manner that ismost suited to their particular needs, with dynamic arrangements beingmade possible through design modularity as described above in FIG. 6.

Another way to detect cyberthreats may be through the continuousmonitoring and analysis of user and device behavioral patterns. Thismethod may be particularly useful when there is little info available onan exploit, for example, a newly developed malware. FIG. 9 is a diagramof an exemplary architecture 800 for a user and entity behavioralanalysis system, according to one aspect. Architecture 800 may comprisea plurality of users 805 a-n, which may be individuals or connecteddevices, connecting to a user and entity behavioral analysis system 810.System 810 may comprise a grouping engine 813, a behavioral analysisengine 819, a monitoring service 822, and a multidimensional time seriesdata store 120 for storing gathered and processed data. Grouping engine813 may be configured to gather and identify user interactions andrelated metrics, which may include volume of interaction, frequency ofinteraction, and the like. Grouping engine 813 may use graph stackservice 145 and DCG module 155 to convert and analyze the data in graphformat. The interaction data may then be used to split users 805 a-ninto a plurality of groups 816 a-n. Groupings may be based ondepartment, project teams, interaction frequency, and other metricswhich may be user-defined. Groupings may not be permanent, and may beadjusted and changed in real-time as group dynamics change. This may beautomated by system 810, or an administrative user may manually changethe groupings.

Behavioral analysis engine 819 may batch process and aggregate overallusage logs, access logs, KERBEROS session data, SAML session data, ordata collected through the use of other network monitoring toolscommonly used in the art such as BRO or SURICATA. The aggregated datamay then be used to generate a behavioral baseline for each groupestablished by grouping engine 813. Behavioral analysis engine 819 mayuse graph stack service 145 and DCG module 155 to convert and analyzethe data in graph format using various machine learning models, and mayalso process the data using parallel computing to quickly process largeamounts of data. Models may be easily added to the system. Behavioralanalysis engine 819 may also be configured to process internalcommunications, such as email, using natural language processing. Thismay provide additional insight into current group dynamics so that amore accurate baseline may be established, or may provide an insightinto health and mood of users.

Monitoring service 822 may actively monitor groups for anomalousbehavior, as based the established baseline. For example, monitoringservice 822 may use the data pipelines of ACDP system 100 ormultidimensional time series data store 120 to conduct real-timemonitoring of various network resource sensors. Aspects that may bemonitored may include, but is not limited to, anomalous web browsing,for example, the number of distinct domains visited exceeding apredefined threshold; anomalous data exfiltration, for example, theamount of outgoing data exceeding a predefined threshold; unusual domainaccess, for example, a subgroup consisting a few members within anestablished group demonstrating unusual browsing behavior by accessingan unusual domain a predetermined number of times within a certaintimeframe; anomalous login times, for example, a user logging into aworkstation during off-hours; unlikely login locations, for example, auser logging in using an account from two distinct locations that may bephysically impossible within a certain timeframe; anomalous serviceaccess, for example, unusual application access or usage pattern; andnew machines, for example, a user logging into a machine or server nottypically accessed.

Detailed Description of Exemplary Aspects

FIG. 30 is a message flow diagram illustrating a valid SAMLauthentication session for a federated service. When a client device4020 attempts to access 4001 a federated service 4030, the serviceprovider redirects the client 4002 to an ADFS server 4010 to request4003 authentication. The ADFS server validates the client's credentialsand returns a signed SAML authentication response 4004 containing anauthentication object that may be presented to the federated serviceprovider to verify the client and grant access to the service 4005. Inthis usage, clients are authenticated prior to any access to a federatedservice, using an ADFS server maintained locally within the client'sdomain in communication with an identity provider such as a KERBEROS™domain controller to enforce authentication for domain clients. Onceauthenticated, a client has access to the federated services outside thedomain, allowing SSO for cloud-based and distributed services.

FIG. 31 is a message flow diagram illustrating a golden SAML attackusing a forged authentication object to gain access to a federatedservice. In a golden SAML attack, a client 4020 first compromises 4101 alocal ADFS server 4010 to acquire the ADFS key used to signauthentication responses (as described above in FIG. 30). The clientthen attempts normal access 4102 to a federated service 4030, whichprompts a redirection to ADFS for authentication 4103. The client thenuses the stolen authentication key to sign a forged authenticationresponse 4104, granting itself access as needed using the stolencredentials obtained from the ADFS server previously. At this point, theclient has now obtained illicit access to the federated service 4105,and can continue to grant itself access to any SAML-enabled serviceusing SSO by simply forging and signing authentication responses asneeded to imitate a legitimately-authenticated session by an ADFSserver.

FIG. 32 is a flow diagram illustrating a method for detecting a goldenSAML attack by comparing IDs in event logs, according to an aspect ofthe invention. When a user authenticates with a federated serviceprovider 921 a-n such as (for example) AWS™ or other cloud-basedproducts or services (as described below, referring to FIG. 2), eventlogs such as (for example, including but not limited to) AWS™ logs orWINDOWS™ event logs (WELs) on an Active Directory Federated Services(ADFS) server, according to what event logs are available for access,may be analyzed to detect SAML forgery attacks such as those using“golden SAML”. Golden SAML attacks use a forged authentication object toauthenticate across SAML-enabled services using SSO, effectively gainingaccess to any federated services without needing to compromise anyparticular accounts or domain controllers, enabling global access tothese services without compromising any particular domain controller orclient devices within a domain. By monitoring and analyzing event logsthese forged authentication objects may be detected, enabling a responseteam to respond quickly to the attack as described below in greaterdetail, with reference to FIG. 33.

According to the method shown in FIG. 32, an event log (such as WEL onan ADFS server, or an event log for a particular federated service suchas AWS™ or MICROSOFT™ OFFICE 365™) may be monitored for activity 3801,such as new events being written to the log (indicating serviceactivity). Logs may be analyzed 3802 either in real-time (that is,analyzing each new event as it is logged) or in batches (such asscheduled analysis of log files, or manually-triggered analysis of adefined set of logs, or other such batch operations), and checked forunique ID tokens such as SAMLAssertionID found in a service's event logas well as a SamlSession GUID found in a Windows event log (WEL) of theidentity provider, as shown in the exemplary SAML code segments below.

The following is an example of a SAML event log record, showing theSAMLAssertionID field containing a unique string that may be checked andcompared against a known valid session to determine if the eventindicates a forged authentication object and thus a golden SAML attack.This record appears in a WEL entry on an ADFS server, where it may bemonitored and analyzed by the operator of the server without needingdirect access to logs for any connected federated services. This enablesany business to monitor for golden SAML attacks on their own ADFSservers, without needing access to logs generated by services operatedby outside entities.

“requestParameters”: { “SAMLAssertionID”:“_e76f6004-cdcb-4580-9a8c-43c160633133”, “roleSessionName”:“sample@email.com”, “durationSeconds”: 3600, “roleArn”:“arn:aws:iam::227545963958:role/ADFS-ec2-readonly”, “principalArn”:“arn:aws:iam::227545963958:saml-provider/idp1” }

These IDs are stored in log entries, both in event logs for a federatedservice (to which a business may not have access, making them lessuseful in detection) or on an ADFS server operated by a business (wherethey may be actively monitored and analyzed easily) as strings that canbe identified with text-based searching and matching, and in a validauthentication request (that is, one wherein a user has a validauthentication object issued by a domain controller that grants themaccess to a service) the IDs will match between the WEL record on theADFS server granting authorization, and the WEL record of the accessresponse from a federated service; that is, the ID that was grantedaccess by the ADFS server should match the ID that was granted access bythe federated service. In a golden SAML attack, a forged authenticationobject produces a mismatch in these IDs (in other words, an ID is givenaccess to a federated service, but no authorization record for the IDexists in the ADFS log because it was forged), so the log records arecompared 3803 to determine if the IDs are a match; if there is amismatch 3805 this indicates a forged authentication object and servesas positive identification of an attack 3804, and if the IDs match theusername (or account ID, or other user-specific unique identifier) maythen be checked 3805 to determine if this user has alreadyauthenticated. If the user has not already authenticated, thenauthentication proceeds as normal 3806; however, if the user has alreadyauthenticated, this duplicate request indicates a forged authenticationobject and identifies an attack wherein the attacker is attempting toduplicate an existing user's credentials 3807.

FIG. 33 is a flow diagram illustrating a method for detecting andmitigating a golden SAML attack using session tagging, according to anaspect of the invention. With session tagging, authentication requestswithin a domain may be tagged with specially-created metadata toidentify each valid session and identify invalid sessions arising from agolden SAML attack. According to the method, when an identity providersuccessfully authenticates a user (e.g., the user's initial login withina domain using SSO for federated services) 3901 a record is created forthe user's authenticated session in an event log 3902. A QOMPLX®security cookie may be created 3903 for the valid authenticationsession, that uniquely identifies that specific authenticated session.The security cookie may then be added to a DC's database 3904 associatedwith the authentication object issued to the user, ensuring that allfuture objects issued for that user now include the QOMPLX® securitycookie. This security cookie may then be checked in event logs 3905, andany invalid records (for example, records of a user authenticatingagainst a federated service, wherein the user's authentication objectwas missing the security cookie) indicate a golden SAML attack 3906wherein an attacker has forged an assertion to gain access, but as aresult has not been issued a valid security cookie. This method ensuresreliable detection of golden SAML attacks by creating additional uniquemetadata for known authenticated sessions, preventing session forgeryand making authentication attempts using forged objects immediatelyobvious.

FIG. 10 is a flow diagram of an exemplary method 1000 for cybersecuritybehavioral analytics, according to one aspect. According to the aspect,behavior analytics may utilize passive information feeds from aplurality of existing endpoints (for example, including but not limitedto user activity on a network, network performance, or device behavior)to generate security solutions. In an initial step 1001, a web crawler115 may passively collect activity information, which may then beprocessed 1002 using a DCG 155 to analyze behavior patterns. Based onthis initial analysis, anomalous behavior may be recognized 1003 (forexample, based on a threshold of variance from an established pattern ortrend) such as high-risk users or malicious software operators such asbots. These anomalous behaviors may then be used 1004 to analyzepotential angles of attack and then produce 1005 security suggestionsbased on this second-level analysis and predictions generated by anaction outcome simulation module 125 to determine the likely effects ofthe change. The suggested behaviors may then be automaticallyimplemented 1006 as needed.

Passive monitoring 1001 then continues, collecting information after newsecurity solutions are implemented 1006, enabling machine learning toimprove operation over time as the relationship between security changesand observed behaviors and threats are observed and analyzed.

This method 1000 for behavioral analytics enables proactive andhigh-speed reactive defense capabilities against a variety ofcyberattack threats, including anomalous human behaviors as well asnonhuman “bad actors” such as automated software bots that may probefor, and then exploit, existing vulnerabilities. Using automatedbehavioral learning in this manner provides a much more responsivesolution than manual intervention, enabling rapid response to threats tomitigate any potential impact. Utilizing machine learning behaviorfurther enhances this approach, providing additional proactive behaviorthat is not possible in simple automated approaches that merely react tothreats as they occur.

FIG. 11 is a flow diagram of an exemplary method 1100 for measuring theeffects of cybersecurity attacks, according to one aspect. According tothe aspect, impact assessment of an attack may be measured using a DCG155 to analyze a user account and identify its access capabilities 1101(for example, what files, directories, devices or domains an account mayhave access to). This may then be used to generate 1102 an impactassessment score for the account, representing the potential risk shouldthat account be compromised. In the event of an incident, the impactassessment score for any compromised accounts may be used to produce a“blast radius” calculation 1103, identifying exactly what resources areat risk as a result of the intrusion and where security personnel shouldfocus their attention. To provide proactive security recommendationsthrough a simulation module 125, simulated intrusions may be run 1104 toidentify potential blast radius calculations for a variety of attacksand to determine 1105 high risk accounts or resources so that securitymay be improved in those key areas rather than focusing on reactivesolutions.

FIG. 12 is a flow diagram of an exemplary method 1200 for continuouscybersecurity monitoring and exploration, according to one aspect.According to the aspect, a state observation service 140 may receivedata from a variety of connected systems 1201 such as (for example,including but not limited to) servers, domains, databases, or userdirectories. This information may be received continuously, passivelycollecting events and monitoring activity over time while feeding 1202collected information into a graphing service 145 for use in producingtime-series graphs 1203 of states and changes over time. This collatedtime-series data may then be used to produce a visualization 1204 ofchanges over time, quantifying collected data into a meaningful andunderstandable format. As new events are recorded, such as changing userroles or permissions, modifying servers or data structures, or otherchanges within a security infrastructure, these events are automaticallyincorporated into the time-series data and visualizations are updatedaccordingly, providing live monitoring of a wealth of information in away that highlights meaningful data without losing detail due to thequantity of data points under examination.

FIG. 13 is a flow diagram of an exemplary method 1300 for mapping acyber-physical system graph (CPG), according to one aspect. According tothe aspect, a cyber-physical system graph may comprise a visualizationof hierarchies and relationships between devices and resources in asecurity infrastructure, contextualizing security information withphysical device relationships that are easily understandable forsecurity personnel and users. In an initial step 1301, behavioranalytics information (as described previously, referring to FIG. 10)may be received at a graphing service 145 for inclusion in a CPG. In anext step 1302, impact assessment scores (as described previously,referring to FIG. 11) may be received and incorporated in the CPGinformation, adding risk assessment context to the behavior information.In a next step 1303, time-series information (as described previously,referring to FIG. 12) may be received and incorporated, updating CPGinformation as changes occur and events are logged. This information maythen be used to produce 1304 a graph visualization of users, servers,devices, and other resources correlating physical relationships (such asa user's personal computer or smartphone, or physical connectionsbetween servers) with logical relationships (such as access privilegesor database connections), to produce a meaningful and contextualizedvisualization of a security infrastructure that reflects the currentstate of the internal relationships present in the infrastructure.

FIG. 14 is a flow diagram of an exemplary method 1400 for continuousnetwork resilience scoring, according to one aspect. According to theaspect, a baseline score can be used to measure an overall level of riskfor a network infrastructure, and may be compiled by first collecting1401 information on publicly-disclosed vulnerabilities, such as (forexample) using the Internet or common vulnerabilities and exploits (CVE)process. This information may then 1402 be incorporated into a CPG asdescribed previously in FIG. 13, and the combined data of the CPG andthe known vulnerabilities may then be analyzed 1403 to identify therelationships between known vulnerabilities and risks exposed bycomponents of the infrastructure. This produces a combined CPG 1404 thatincorporates both the internal risk level of network resources, useraccounts, and devices as well as the actual risk level based on theanalysis of known vulnerabilities and security risks.

FIG. 15 is a flow diagram of an exemplary method 1500 for cybersecurityprivilege oversight, according to one aspect. According to the aspect,time-series data (as described above, referring to FIG. 12) may becollected 1501 for user accounts, credentials, directories, and otheruser-based privilege and access information. This data may then 1502 beanalyzed to identify changes over time that may affect security, such asmodifying user access privileges or adding new users. The results ofanalysis may be checked 1503 against a CPG (as described previously inFIG. 13), to compare and correlate user directory changes with theactual infrastructure state. This comparison may be used to performaccurate and context-enhanced user directory audits 1504 that identifynot only current user credentials and other user-specific information,but changes to this information over time and how the user informationrelates to the actual infrastructure (for example, credentials thatgrant access to devices and may therefore implicitly grant additionalaccess due to device relationships that were not immediately apparentfrom the user directory alone).

FIG. 16 is a flow diagram of an exemplary method 1600 for cybersecurityrisk management, according to one aspect. According to the aspect,multiple methods described previously may be combined to provide liveassessment of attacks as they occur, by first receiving 1601 time-seriesdata for an infrastructure (as described previously, in FIG. 12) toprovide live monitoring of network events. This data is then enhanced1602 with a CPG (as described above in FIG. 13) to correlate events withactual infrastructure elements, such as servers or accounts. When anevent (for example, an attempted attack against a vulnerable system orresource) occurs 1603, the event is logged in the time-series data 1604,and compared against the CPG 1605 to determine the impact. This isenhanced with the inclusion of impact assessment information 1606 forany affected resources, and the attack is then checked against abaseline score 1607 to determine the full extent of the impact of theattack and any necessary modifications to the infrastructure orpolicies.

FIG. 17 is a flow diagram of an exemplary method 1700 for mitigatingcompromised credential threats, according to one aspect. According tothe aspect, impact assessment scores (as described previously, referringto FIG. 11) may be collected 1701 for user accounts in a directory, sothat the potential impact of any given credential attack is known inadvance of an actual attack event. This information may be combined witha CPG 1702 as described previously in FIG. 13, to contextualize impactassessment scores within the infrastructure (for example, so that it maybe predicted what systems or resources might be at risk for any givencredential attack). A simulated attack may then be performed 1703 to usemachine learning to improve security without waiting for actual attacksto trigger a reactive response. A blast radius assessment (as describedabove in FIG. 11) may be used in response 1704 to determine the effectsof the simulated attack and identify points of weakness, and produce arecommendation report 1705 for improving and hardening theinfrastructure against future attacks.

FIG. 18 is a flow diagram of an exemplary method 1800 for dynamicnetwork and rogue device discovery, according to one aspect. Accordingto the aspect, an advanced cyber decision platform may continuouslymonitor a network in real-time 1801, detecting any changes as theyoccur. When a new connection is detected 1802, a CPG may be updated 1803with the new connection information, which may then be compared againstthe network's resiliency score 1804 to examine for potential risk. Theblast radius metric for any other devices involved in the connection mayalso be checked 1805, to examine the context of the connection for riskpotential (for example, an unknown connection to an internal data serverwith sensitive information may be considered a much higher risk than anunknown connection to an externally-facing web server). If theconnection is a risk, an alert may be sent to an administrator 1806 withthe contextual information for the connection to provide a concisenotification of relevant details for quick handling.

FIG. 19 is a flow diagram of an exemplary method 1900 for attackdetection, according to one aspect. To detect attacks, behavioralanalytics may be employed to detect forged AO's, whether from incorrectconfiguration or from an attack. According to the aspect, an advancedcyber decision platform may continuously monitor a network 1901,informing a CPG in real-time of all traffic associated with people,places, devices, or services 1902. Machine learning algorithms detectbehavioral anomalies as they occur in real-time 1903, notifyingadministrators with an assessment of the anomalous event 1904 as well asa blast radius score for the particular event and a network resiliencyscore to advise of the overall health of the network. By automaticallydetecting unusual behavior and informing an administrator of the anomalyalong with contextual information for the event and network, a potentialattack is immediately detected when a new authentication connection ismade.

FIG. 20 is a flow diagram of an exemplary method 2000 for risk-basedvulnerability and patch management, according to one aspect. Accordingto the aspect, an advanced cyber decision platform may monitor allinformation about a network 2001, including (but not limited to) devicetelemetry data, log files, connections and network events, deployedsoftware versions, or contextual user activity information. Thisinformation is incorporated into a CPG 2002 to maintain an up-to-datemodel of the network in real-time. When a new vulnerability isdiscovered, a blast radius score may be assessed 2003 and the network'sresiliency score may be updated 2004 as needed. A security alert maythen be produced 2005 to notify an administrator of the vulnerabilityand its impact, and a proposed patch may be presented 2006 along withthe predicted effects of the patch on the vulnerability's blast radiusand the overall network resiliency score. This determines both the totalimpact risk of any particular vulnerability, as well as the overalleffect of each vulnerability on the network as a whole. This continuousnetwork assessment may be used to collect information about newvulnerabilities and exploits to provide proactive solutions with clearresult predictions, before attacks occur.

FIG. 21 is a flow diagram of an exemplary method 2100 for establishinggroups of users according to one aspect. At an initial step 2103, datapertaining to network interaction between users and devices are gatheredby a grouping engine. At step 2106, the grouping engine may then processthe gathered information by converting it to a graph format and usingDCG module to establish groupings for users. A system administrator mayprovide additional input, and fine-tune the groupings if required. Atstep 2109, a behavioral baseline is established for each group that maybe based on the interaction information, network logs, connecteddevices, and the like. At step 2112, groups are continuous monitored foranomalous behavior.

FIG. 22 is a flow diagram of an exemplary method 2200 for monitoringgroups for anomalous behavior, according to one aspect. At an initialstep 2203, a system, as described above in FIG. 8, gathersnetwork-related data. This data may comprise usage logs, Kerberossessions data, SAML sessions data, computers and other devices connectedto the network, active users, software installed, and the like. At step2206, a behavioral analysis engine may process the data. Parallelcomputing may be used to speed up the processing of the data. The datamay then be sorted by, and associated to, previously establishedgroupings. At step 2209, a behavioral baseline score is generated foreach group based on the results of the data processing. At step 2212,the data is stored into a time-series graph database. The processrepeats periodically to create snapshots of various moments in time, andstored into the database. This may allow the system to retrain thebaseline to take into considering non-anomalous baseline variances thatmay occur over time, as well as forecast changes in group dynamics usingpredictive analysis functions of ACDP system 100.

FIG. 23 is a flow diagram for an exemplary method 2300 for handing adetection of anomalous behavior, according to one aspect. At an initialstep 2303, the system detects anomalous user behavior from a group. Thismay be based on comparison to established baselines, or a high priorityincident caught during routine monitoring, for example a deviceaccessing a blacklisted domain. At step 2306, the system investigatesthe group in which the anomalous behavior originated. This may include amore thorough analysis of usage and access logs. If applicable, users ordevices with higher access privileges may be investigated before thosewith lower access privileges. At step 2309, the source or sources of theanomalous behavior is identified, and some corrective measures may betaken. For example, the offending device or user account may beautomatically locked out of the network until a solution has beenimplemented. At step 2312, group members and system administrators maybe notified. The system may utilize the various techniques discussedabove to recommend a corrective action, or the system may take actionautomatically.

FIG. 24 is a flow diagram illustrating an exemplary method 2400 forprocessing a new user connection, according to one aspect. At an initialstep 2403, system 910 detects a user connecting to a monitored serviceprovider. At step 2406, if the user is connecting with an existing AO,the process leads to the method discussed in FIG. 25 at step 2409.

If the user doesn't have an existing AO, the service provider forwardsthe user to an identity provider at step 2412. At step 2415, theidentity provider prompts the user for identifying information, such asa username and password. At step 2418, after successful verification,the IdP generates a unique AO for the user. At step 2421, system 910retrieves the AO and uses a hashing engine to calculate a cryptographichash for the newly generated AO, and stores the hash in a data store.

FIG. 25 is a flow diagram illustrating an exemplary method 2500 forverifying the authenticity of an authentication object, according to oneaspect. At an initial step 2503, a user with an AO connects to amonitored service provider. At step 2506, system 910 detects theconnection request, retrieves the AO, and generates a cryptographic hashfor the AO. System 910 may now compare the newly generated hashes withprevious generated hashes stored in memory. At step 2509, if the AO isfound to be authentic, the connect proceeds as normal and method 2500ends at step 2512 as no further action for this session is required. Ifthe AO is determined to be forged, method 2500 goes to step 2515 whereECA rules may be triggered to perform their preset functions, andperform “circuit breaker” checks within a user-configurable time period.At step 2518, a network administrator at step may be notified, and sentany relevant information, such as blast radius, access logs for theforged AO connection, and the like.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 26, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™ THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 26 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 27, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 26). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 28, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 27. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 29 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to keyboard 49, pointing device 50,hard disk 52, and real-time clock 51. NIC 53 connects to network 54,which may be the Internet or a local network, which local network may ormay not have connections to the Internet. Also shown as part of system40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for detecting and mitigating golden SAMLattacks against federated services, comprising: an authentication objectinspector comprising at least a processor, a memory, and a plurality ofprogramming instructions stored in the memory and operating on theprocessor, wherein the programmable instructions, when operating on theprocessor, cause the processor to: receive network traffic comprising aplurality of network packets, the plurality of network packetscomprising at least a first authentication object known to be generatedby an identity provider associated with a federated service; store arecord of the first authentication object, with attached metadatacomprising at least a timestamp of when the authentication object wasreceived, in a time-series database; generate a security cookie for thefirst authentication object using a hashing engine; provide the securitycookie to the identity provider from which the first authenticationobject was generated; receive a request for access to the federatedservice accompanied by a second authentication object; compare a valueof an ID string within the second authentication object against a valueof a corresponding ID string within the stored record of the firstauthentication object; check the second authentication object for avalid security cookie; and a hashing engine comprising a secondplurality of programming instructions stored in the memory of, andoperating on the processor of, the computing device, wherein the secondplurality of programmable instructions, when operating on the processor,cause the computing device to: receive authentication objects from theauthentication object inspector; calculate a security cookie for eachauthentication object received by performing at least a plurality ofcalculations and transformations on each authentication object received;and return the security cookie for each authentication object receivedto the authentication object inspector.
 2. The system of claim 1,wherein the authentication object inspector is operated by the identityprovider.
 3. The system of claim 1, wherein the authentication objectinspector is operated by a client device communicating with the identityprovider over a network.
 4. A method for detecting golden SAML attacksagainst federated services, comprising the steps of: (a) receiving afirst authentication object at an authentication object inspector, theauthentication object being generated by an identity provider; (b)generating a security cookie for the first authentication object using ahashing engine; (c) providing the security cookie to the identityprovider from which the first authentication object was generated; (d)receiving a request for access to a federated service accompanied by asecond authentication object; and (e) checking the second authenticationobject for a valid security cookie.
 5. The method of claim 4, whereinthe identity provider includes the security cookie in additionalauthentication objects issued to the same user to which the firstauthentication object was issued.
 6. The method of claim 4, wherein amissing or invalid security cookie results in authentication failure.